{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PrithviWxC Rollout Inference\n",
    "If you haven't already, take a look at the exmaple for the PrithviWxC core\n",
    "model, as we will pass over the points covered there.\n",
    "\n",
    "Here we will introduce the PrithviWxC model that was trained furhter for\n",
    "autoregressive rollout, a common strategy to increase accuracy and stability of\n",
    "models when applied to forecasting-type tasks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "from huggingface_hub import hf_hub_download, snapshot_download\n",
    "\n",
    "# Set backend etc.\n",
    "torch.jit.enable_onednn_fusion(True)\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.benchmark = True\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "# Set seeds\n",
    "random.seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(42)\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Set device\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "# Set variables\n",
    "surface_vars = [\n",
    "    \"EFLUX\",\n",
    "    \"GWETROOT\",\n",
    "    \"HFLUX\",\n",
    "    \"LAI\",\n",
    "    \"LWGAB\",\n",
    "    \"LWGEM\",\n",
    "    \"LWTUP\",\n",
    "    \"PS\",\n",
    "    \"QV2M\",\n",
    "    \"SLP\",\n",
    "    \"SWGNT\",\n",
    "    \"SWTNT\",\n",
    "    \"T2M\",\n",
    "    \"TQI\",\n",
    "    \"TQL\",\n",
    "    \"TQV\",\n",
    "    \"TS\",\n",
    "    \"U10M\",\n",
    "    \"V10M\",\n",
    "    \"Z0M\",\n",
    "]\n",
    "static_surface_vars = [\"FRACI\", \"FRLAND\", \"FROCEAN\", \"PHIS\"]\n",
    "vertical_vars = [\"CLOUD\", \"H\", \"OMEGA\", \"PL\", \"QI\", \"QL\", \"QV\", \"T\", \"U\", \"V\"]\n",
    "levels = [\n",
    "    34.0,\n",
    "    39.0,\n",
    "    41.0,\n",
    "    43.0,\n",
    "    44.0,\n",
    "    45.0,\n",
    "    48.0,\n",
    "    51.0,\n",
    "    53.0,\n",
    "    56.0,\n",
    "    63.0,\n",
    "    68.0,\n",
    "    71.0,\n",
    "    72.0,\n",
    "]\n",
    "padding = {\"level\": [0, 0], \"lat\": [0, -1], \"lon\": [0, 0]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lead time\n",
    "When performing auto-regressive rollout, the intermediate steps require the\n",
    "static data at those times and---if using `residual=cliamte`---the intermediate\n",
    "climatology. We provide a dataloader that extends the MERRA2 loader of the\n",
    "core model, adding in these additional terms. Further, it return target data for\n",
    "the intermediate steps if those are required for loss terms. \n",
    "\n",
    "The `lead_time` flag still lets the target time for the model, however now it\n",
    "only a single value and must be a positive integer multiple of the `-input_time`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead_time = 12  # This variable can be change to change the task\n",
    "input_time = -6  # This variable can be change to change the task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data file\n",
    "MERRA-2 data is available from 1980 to the present day,\n",
    "at 3-hour temporal resolution. The dataloader we have provided\n",
    "expects the surface data and vertical data to be saved in\n",
    "separate files, and when provided with the directories, will\n",
    "search for the relevant data that falls within the provided time range.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = (\"2020-01-01T00:00:00\", \"2020-01-01T23:59:59\")\n",
    "\n",
    "surf_dir = Path(\"./merra-2\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"merra-2/MERRA2_sfc_2020010[1].nc\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_dir = Path(\"./merra-2\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"merra-2/MERRA_pres_2020010[1].nc\",\n",
    "    local_dir=\".\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climatology\n",
    "The PrithviWxC model was trained to calculate the output by\n",
    "producing a perturbation to the climatology at the target time.\n",
    " This mode of operation is set via the `residual=climate` option.\n",
    " This was chosen as climatology is typically a strong prior for\n",
    " long-range prediction. When using the `residual=climate` option,\n",
    " we have to provide the dataloader with the path of the\n",
    " climatology data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "surf_clim_dir = Path(\"./climatology\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"climatology/climate_surface_doy00[1]*.nc\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_clim_dir = Path(\"./climatology\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"climatology/climate_vertical_doy00[1]*.nc\",\n",
    "    local_dir=\".\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "positional_encoding = \"fourier\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataloader init\n",
    "We are now ready to instantiate the dataloader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2_rollout import Merra2RolloutDataset\n",
    "\n",
    "dataset = Merra2RolloutDataset(\n",
    "    time_range=time_range,\n",
    "    lead_time=lead_time,\n",
    "    input_time=input_time,\n",
    "    data_path_surface=surf_dir,\n",
    "    data_path_vertical=vert_dir,\n",
    "    climatology_path_surface=surf_clim_dir,\n",
    "    climatology_path_vertical=vert_clim_dir,\n",
    "    surface_vars=surface_vars,\n",
    "    static_surface_vars=static_surface_vars,\n",
    "    vertical_vars=vertical_vars,\n",
    "    levels=levels,\n",
    "    positional_encoding=positional_encoding,\n",
    ")\n",
    "assert len(dataset) > 0, \"There doesn't seem to be any valid data.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "### Scalers and other hyperparameters\n",
    "Again, this setup is similar as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2 import (\n",
    "    input_scalers,\n",
    "    output_scalers,\n",
    "    static_input_scalers,\n",
    ")\n",
    "\n",
    "surf_in_scal_path = Path(\"./climatology/musigma_surface.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{surf_in_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_in_scal_path = Path(\"./climatology/musigma_vertical.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{vert_in_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "surf_out_scal_path = Path(\"./climatology/anomaly_variance_surface.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{surf_out_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_out_scal_path = Path(\"./climatology/anomaly_variance_vertical.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{vert_out_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "in_mu, in_sig = input_scalers(\n",
    "    surface_vars,\n",
    "    vertical_vars,\n",
    "    levels,\n",
    "    surf_in_scal_path,\n",
    "    vert_in_scal_path,\n",
    ")\n",
    "\n",
    "output_sig = output_scalers(\n",
    "    surface_vars,\n",
    "    vertical_vars,\n",
    "    levels,\n",
    "    surf_out_scal_path,\n",
    "    vert_out_scal_path,\n",
    ")\n",
    "\n",
    "static_mu, static_sig = static_input_scalers(\n",
    "    surf_in_scal_path,\n",
    "    static_surface_vars,\n",
    ")\n",
    "\n",
    "residual = \"climate\"\n",
    "masking_mode = \"local\"\n",
    "decoder_shifting = True\n",
    "masking_ratio = 0.99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model init\n",
    "We can now build and load the pretrained weights, note that you should use the\n",
    "rollout version of the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_path = Path(\"./weights/prithvi.wxc.rollout.2300m.v1.pt\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.rollout.2300m.v1\",\n",
    "    filename=weights_path.name,\n",
    "    local_dir=\"./weights\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.model import PrithviWxC\n",
    "\n",
    "model = PrithviWxC(\n",
    "    in_channels=160,\n",
    "    input_size_time=2,\n",
    "    in_channels_static=8,\n",
    "    input_scalers_mu=in_mu,\n",
    "    input_scalers_sigma=in_sig,\n",
    "    input_scalers_epsilon=0.0,\n",
    "    static_input_scalers_mu=static_mu,\n",
    "    static_input_scalers_sigma=static_sig,\n",
    "    static_input_scalers_epsilon=0.0,\n",
    "    output_scalers=output_sig**0.5,\n",
    "    n_lats_px=360,\n",
    "    n_lons_px=576,\n",
    "    patch_size_px=[2, 2],\n",
    "    mask_unit_size_px=[30, 32],\n",
    "    mask_ratio_inputs=masking_ratio,\n",
    "    embed_dim=2560,\n",
    "    n_blocks_encoder=12,\n",
    "    n_blocks_decoder=2,\n",
    "    mlp_multiplier=4,\n",
    "    n_heads=16,\n",
    "    dropout=0.0,\n",
    "    drop_path=0.0,\n",
    "    parameter_dropout=0.0,\n",
    "    residual=residual,\n",
    "    masking_mode=masking_mode,\n",
    "    decoder_shifting=decoder_shifting,\n",
    "    positional_encoding=positional_encoding,\n",
    "    checkpoint_encoder=[],\n",
    "    checkpoint_decoder=[],\n",
    ")\n",
    "\n",
    "\n",
    "state_dict = torch.load(weights_path, weights_only=False)\n",
    "if \"model_state\" in state_dict:\n",
    "    state_dict = state_dict[\"model_state\"]\n",
    "model.load_state_dict(state_dict, strict=True)\n",
    "\n",
    "if (hasattr(model, \"device\") and model.device != device) or not hasattr(\n",
    "    model, \"device\"\n",
    "):\n",
    "    model = model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rollout\n",
    "We are now ready to perform the rollout. Agin the data has to be run through a\n",
    "preprocessor. However this time we use a preprocessor that can handle the\n",
    "additional intermediate data. Also, rather than calling the model directly, we\n",
    "have a conveient wrapper function that performs the interation. This also\n",
    "simplifies the model loading when using a sharded cahckpoint. If you attempt to\n",
    "perform training steps upton this function, we should use an aggressive number\n",
    "of activation checkpoints as the memory consumption becomes quite high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2_rollout import preproc\n",
    "from PrithviWxC.rollout import rollout_iter\n",
    "\n",
    "data = next(iter(dataset))\n",
    "batch = preproc([data], padding)\n",
    "\n",
    "for k, v in batch.items():\n",
    "    if isinstance(v, torch.Tensor):\n",
    "        batch[k] = v.to(device)\n",
    "\n",
    "rng_state_1 = torch.get_rng_state()\n",
    "with torch.no_grad():\n",
    "    model.eval()\n",
    "    out = rollout_iter(dataset.nsteps, model, batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2m = out[0, 12].cpu().numpy()\n",
    "\n",
    "lat = np.linspace(-90, 90, out.shape[-2])\n",
    "lon = np.linspace(-180, 180, out.shape[-1])\n",
    "X, Y = np.meshgrid(lon, lat)\n",
    "\n",
    "plt.contourf(X, Y, t2m, 100)\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "wfm_vevn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
